Abstract
Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation(TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.
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Keywords
- Linear Discriminant Analysis
- Transcatheter Aorta Valve Implantation
- Pigtail Catheter
- Multiple Instance Learning
- Fisher Discriminant Analysis
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References
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)
Brost, A., Liao, R., Hornegger, J., Strobel, N.: 3-D Respiratory Motion Compensation during EP Procedures by Image-Based 3-D Lasso Catheter Model Generation and Tracking. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 394–401. Springer, Heidelberg (2009)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. on PAMI 27(10), 1631–1643 (2005)
Grabner, M., Grabner, H., Bischof, H.: Learning features for tracking. In: CVPR (2007)
Karar, M.E., John, M., Holzhey, D., Falk, V., Mohr, F.-W., Burgert, O.: Model-Updated Image-Guided Minimally Invasive Off-Pump Transcatheter Aortic Valve Implantation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 275–282. Springer, Heidelberg (2011)
Lin, R.S., Yang, M.H., Levinson, S.: Object tracking using incremental Fisher discriminant analysis. In: ICPR, vol. 2, pp. 757–760 (2004)
Matthews, I., Ishikawa, T., Baker, S.: The template update problem. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(6), 810–815 (2004)
Ross, D., Lim, J., Lin, R.S., Yang., M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision Special Issue: Learning for Vision (2007)
Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)
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Wang, P., Zheng, Y., John, M., Comaniciu, D. (2012). Catheter Tracking via Online Learning for Dynamic Motion Compensation in Transcatheter Aortic Valve Implantation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_3
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DOI: https://doi.org/10.1007/978-3-642-33418-4_3
Publisher Name: Springer, Berlin, Heidelberg
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